Spoilt - Ocean Cleanup: Alternative logistics chains to accommodate plastic waste recycling: An economic evaluation
Bibliographic record
Abstract
Every year about 300 million tons of plastic is produced, resulting in more than five trillion plastic particles currently floating in the oceans five largest convergence zones. The Ocean Cleanup is testing a method to passively collect this floating plastic debris, transport, recycle, process and sell it. The purpose of this paper is to evaluate alternative logistics chains to accommodate ocean plastic waste recycling by connecting transport with data collection and data analytics. The scenarios are based on different geographical destinations, supply chain lengths and types, and offered local development opportunities. A new reverse logistics channel dedicated to the Ocean Cleanup is developed, as existing reverse logistics supply chains are not able to capture the specifics of the plastic waste collection. Performances of the different scenarios are assessed by collecting data (on plastic volumes collected from the Ocean, on usage of plastics as a resource, and on transport cost) and usage of a detailed integrated model which enables a performance comparison of different logistical structures on logistics costs and on plastics production outputs. The cheapest and most disappointing solution would be to do nothing. However, the analysis shows that more complicated logistic structures whereby the collected plastic waste is used to produce glasses, socks, and carpets can lead to sustainable business models for cleaning up the Oceans. If the focus would be only on cost, the best model would be to minimize the transport distance and focus on San Francisco as closest port for the selected gyre to be analyzed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".